SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 1226–1250 of 1718 papers

TitleStatusHype
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RLCode0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning—0
The Power of Communication in a Distributed Multi-Agent System—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents—0
Real-world challenges for multi-agent reinforcement learning in grid-interactive buildings—0
Distributed Policy Gradient with Variance Reduction in Multi-Agent Reinforcement Learning—0
Application of Multi-Agent Reinforcement Learning for Battery Management in Renewable Mini-Grids—0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS—0
Multi-lingual agents through multi-headed neural networksCode0
Off-Policy Correction For Multi-Agent Reinforcement LearningCode0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods—0
Polymatrix Competitive Gradient Descent—0
Interactive Medical Image Segmentation with Self-Adaptive Confidence Calibration—0
Relative Distributed Formation and Obstacle Avoidance with Multi-agent Reinforcement Learning—0
Collaboration Promotes Group Resilience in Multi-Agent AI—0
Causal Multi-Agent Reinforcement Learning: Review and Open Problems—0
Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium controlCode0
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic—0
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement LearningCode0
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified